Publications
Reichelt, T. et al. (2022) “Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently”, in Proceedings of Machine Learning Research, pp. 1676–1685.
He, Y. et al. (2022) “MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian”, in Proceedings of Machine Learning Research.
Ghalebikesabi, S. et al. (2022) “Mitigating Statistical Bias within Differentially Private Synthetic Data”, in Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022, pp. 696–705.
Reichelt, T., Ong, L. and Rainforth, T. (2022) “Rethinking Variational Inference for Probabilistic Programs with Stochastic Support”, in Advances in Neural Information Processing Systems.
Kossen, J. et al. (2022) “Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation”, in Advances in Neural Information Processing Systems.
Barrett, B. et al. (2022) “Certifiably Robust Variational Autoencoders”, in Proceedings of Machine Learning Research, pp. 3663–3683.
Miao, N. et al. (2022) “ON INCORPORATING INDUCTIVE BIASES INTO VAES”, in Iclr 2022 10th International Conference on Learning Representations.
Reichelt, T. et al. (2022) “Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently”, in Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022, pp. 1676–1685.
Miao, N. et al. (2022) “ON INCORPORATING INDUCTIVE BIASES INTO VAES”, in Iclr 2022 10th International Conference on Learning Representations.